Fellow: Myrte Wennen, Supervisor: Oliver Gurney-Champion
Cancer patients can greatly benefit from precision medicine, where quantitative magnetic resonance imaging (qMRI) biomarkers are used to select optimal personalized treatment. Unfortunately, the application of qMRI faces challenges in accuracy, precision, and reproducibility. Consequently, despite many promising results, qMRI is not integrated in clinical care. Our research has shown that deep learning can drastically improve the accuracy and consistency of qMRI techniques. At this moment, these deep learning techniques are not utilized efficiently, which greatly hinders implementation in clinical and research workflows. With this Fellowship, I aim to enhance precision medicine for cancer patients by integrating deep learning techniques for qMRI directly on MR scanners and into clinical and research workflows. We will form an interdisciplinary team to streamline adoption in clinical and research practice, thereby accelerating research and improving clinical implementation. We will apply the techniques in two clinical trials investigating treatment response in patients with cervical and head and neck cancer.